System, computer program product and method for generating embeddings of textual and quantitative data

ABSTRACT

A method, computer program product and computer system is disclosed that generates a set of distributed representation vectors from a dataset of textual and non-text data. In one method, a computer system receives a dataset, cleans the received dataset, parses the cleaned dataset to identify known classes of data, extracts data elements from the dataset based on the known classes of data, organizes the extracted data elements into one or more records, compiles a dictionary of unique data elements and associated codes from the one or more records, creates a set of training pairs using permutations of the codes that correspond to data elements within each record, and computes a distributed representation vector for each of the data elements in the dictionary using the set of training pairs.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation of U.S. application Ser. No.16/050,790, filed Jul. 31, 2018, the entire contents of which areincorporated herein by reference.

BACKGROUND OF THE INVENTION Field of the Invention

The present disclosure generally relates to the field of data andnatural language processing, and more particularly to systems andmethods for learning embeddings of data elements such as words, phrases,sentences, quantitative, and other classes of data for determining thesimilarity between and among data objects containing one or more dataelements.

Description of the Related Art

Natural language processing (NLP) is a multidisciplinary field involvingaspects of computer science, artificial intelligence, and linguisticsthat, among other things, is concerned with using computers to derivemeaning from, and allow operations on, natural (human) language text.

NLP systems may perform many different tasks, including, but not limitedto, determining the relationship between and among text strings (words,phrases and sentences, for example). A known way to determine therelationship between and among words, phrases or sentences is to comparetheir respective embeddings. An embedding is a mapping of naturallanguage text to a vector of real numbers in a vector space (called“word vectors” or “distributed representation vectors”). Embeddingusually involves mapping from a vector space of high dimensionality(e.g., one dimension per word, phrase or sentence—the size of avocabulary or dictionary) to a vector space with a much lowerdimensionality. Typically, the embeddings of similar or related words,phrases or sentences are located “close” to each other in the vectorspace.

SUMMARY OF THE INVENTION

According to an aspect of the present disclosure, there is a method,computer program product and/or computer system that generates a set ofdistributed representation vectors by receiving a dataset, wherein thedataset comprises a set of related items of at least one of naturallanguage text and non-text data; cleaning the received dataset; parsingthe cleaned dataset to identify known classes of data; extracting dataelements from the dataset based on the known classes of data; organizingthe extracted data elements into one or more records; compiling adictionary of unique data elements and associated codes from the one ormore records; creating a set of training pairs using permutations of thecodes that correspond to data elements within each record; and computinga distributed representation vector for each of the data elements in thedictionary using the set of training pairs.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete appreciation of the invention and many of the attendantadvantages thereof will be readily obtained as the same becomes betterunderstood by reference to the following detailed description whenconsidered in connection with the accompanying drawings, wherein:

FIG. 1 is a flow diagram showing an embodiment of a process according tothe present disclosure;

FIG. 2 is an illustration of an example database record according to anembodiment of the present disclosure;

FIG. 3 is an illustration of an example dictionary according to anembodiment of the present disclosure;

FIG. 4 is an illustration of a set of training input/output pairsaccording to an embodiment of the present disclosure;

FIG. 5 is a block diagram showing a neural network according to anembodiment of the present disclosure;

FIG. 6 is an illustration of an example weight matrix according to anembodiment of the present disclosure;

FIG. 7 illustrates a simplified vector space view according to anembodiment of the present disclosure;

FIG. 8 is a functional block diagram illustrating a networked systemaccording to an embodiment of the present disclosure; and

FIG. 9 is an illustration of an example item of natural language textaccording to an embodiment of the present disclosure;

FIG. 10 is an illustration of another example database record accordingto an embodiment of the present disclosure;

FIG. 11 is an illustration of another example dictionary according to anembodiment of the present disclosure;

FIG. 12 is an illustration of another set of training input/output pairsaccording to an embodiment of the present disclosure;

FIG. 13 is an illustration of an example of converting randomizedtraining input/output into “one-hot” vectors; and

FIG. 14 is an illustration of another example weight matrix according toan embodiment of the present disclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

In a data processing system, it may be useful to represent dataelements, including words, phrases and sentences found in naturallanguage text, and non-text data such as quantitative data, dates,timestamps, computer instructions, hypertext, status flags, binarycodes, geospatial data, identification codes, other alphanumericstrings, and the like, as vectors in a vector space. This may allow forthe comparison of data objects containing one or more data elements bydetermining the proximity of their representations in the vector space.Various methods for mapping words, phrases or sentences into lowerdimensional vectors (embedding) are known, but typically these methodsuse some form of linguistic context of the words (e.g., words in asentence and within a short distance of each other). Embodiments in thepresent disclosure may implement embedding, instead, by associating alltypes of data, including natural language text (such as words, phrasesand sentences) and, also, quantitative data (numeric information) and/orother types of non-text data (such as dates, timestamps, computerinstructions, hypertext, status flags, binary codes, geospatial data,identification codes, other alphanumeric strings, or the like) that arewithin a common data object (e.g., a database record, or the like).

The flow diagrams and block diagrams in the figures illustrate exemplary(and in some cases, simplified) architecture, functionality, andoperation of possible implementations of systems, methods, and computerprogram products according to various embodiments in the presentdisclosure. In the diagrams and the associated descriptions, each blockin a flow diagram or block diagram may represent a module, segment, orgroup of instructions that may be used for implementing the specifiedfunction. In some alternative embodiments, the functions noted in theflow diagrams or block diagrams may occur in an order different fromthat illustrated in the figures without affecting overall functionality.For example, two blocks shown in succession may be executedconcurrently, or the blocks may, in some embodiments, be executed in thereverse order, depending upon the functionality involved. It should alsobe noted that each block of the block diagrams and/or flow diagrams, andcombinations of blocks in the block diagrams and/or flow diagrams, canbe implemented by special purpose hardware-based systems that performthe specified functions or consist of a combination of special purposehardware and computer instructions.

One way to determine similarity between data objects containing one ormore data elements of varying types of data, including natural languagetext (such as words, phrases, sentences), quantitative data, and othertypes of non-text data (such as dates, timestamps, computerinstructions, hypertext, status flags, binary codes, geospatial data,identification codes, other alphanumeric strings, or the like) in a dataprocessing system is to compare vector representations of those dataobjects and their component data elements. As stated above, embodimentsof the present disclosure (including the present embodiment) maygenerate vector representations for any data element (or combination ofdata elements), which can include natural language text (such as words,phrases and sentences), quantitative data, and other types of non-textdata (such as dates, timestamps, computer instructions, hypertext,status flags, binary codes, geospatial data, identification codes, otheralphanumeric strings, or the like) using associations within a commondata object (a database record, for example).

Referring now to the drawings, wherein like reference numerals designateidentical or corresponding parts throughout the several views, FIG. 1illustrates a flow diagram depicting a process 100 according to anembodiment of the present disclosure. Processing begins at step S105,where a process 100 may receive a source dataset. The source dataset maycomprise a set of related items of natural language text, quantitativedata and/or other types of data. In some embodiments, the items ofnatural language text may include single words, phrases or sentences. Inother embodiments, the items of natural language text may include entireparagraphs, groups of paragraphs or documents. Other types of data caninclude dates, timestamps, computer instructions, hypertext, statusflags, binary codes, geospatial data, identification codes, otheralphanumeric strings, and the like. Examples of such related itemsinclude, but are not limited to, job postings, résumés, web pages,product descriptions, technical articles, product reviews, personnelrecords, materials records, transaction logs, purchase orders, financialreports, corporate information, and recipes. In an embodiment, jobposting data may be obtained from one or more web servers having jobpostings provided by, for example, corporations, non-profitorganizations and government agencies. For example, job posting data maybe obtained from job search and professional networking websites such asCareerBuilder, Glassdoor, Idealist, Indeed, LinkedIn, Monster, and thelike. Job posting data may also be obtained directly from corporate,organization or government agency websites (i.e., www.usajobs.gov).

In step S110, process 100 may clean the source dataset and extract dataelements. Cleaning of the source dataset may involve, for example,removing extraneous, irrelevant or inaccurate text and non-text datafrom the source dataset. Cleaning may also involve correcting corrupt orinaccurate data in the dataset. Cleaning may be performed automaticallyor interactively with appropriate data cleaning tools.

The extraction of data elements may involve, for example, parsing thesource dataset to identify known classes of data based on a consistentarchitecture and classification taxonomy. In an embodiment, data withina job posting, for example, may be classified as “Company/OrganizationName,” “Job Title,” “Job Location,” “Salary,” “Job ID,” “Posting Date,”and additional fields (“Tags”) like job description, required educationand level, required experience, and other descriptive information. Insome embodiments, some of the data classes may be defined asquantitative data (having numeric quantities) or other non-text types ofdata. For example, classes such as “Salary” may be defined asquantitative data, classes such as “Job ID” can be alphanumericidentifiers, classes such as “Posting Date” can be timestamps, andclasses such as “Job Location” can include ZIP code and geospatial data.

In step S115, process 100 may organize the extracted data elements intodatabase records stored in a memory or other storage device (computerreadable storage medium). Each database record may include one or morecategorized fields. Each field may contain one or more sub-fields. Eachsubfield may contain a number (e.g., integer or real number), or a textstring (one or more characters of alphanumeric text). Subfields may alsocontain “pointers” (e.g., a reference to another database record),Boolean values (“1” or “0”) or other types of data. In an embodiment,job posting data may be organized into records similar to thatillustrated in FIG. 2.

In FIG. 2, record 200 may include fields 205-213 (each field representedby a row of the array shown in FIG. 2). In an embodiment, field 205 mayrelate to a location of a posted job, field 206 may relate to acompany/organization name associated with a posted job, field 207 mayrelate to a salary associated with a posted job, field 208 may relate toa job title associated with a posted job, and fields 209-213 may relateto additional data (tags) associated with a posted job. Each of thefields 205-213 within record 200 may also include subfields 215-218(each subfield represented by a column of the array shown in FIG. 2). Inan embodiment, fields 205-213 may include subfields that identify ajob_id 220, a code 225, an element 230, and a category 235.

Returning again to FIG. 1, in step S120, process 100 may compile adictionary, stored in a memory or other storage device (computerreadable storage medium), of unique data elements (no two data elementsin the dictionary may be identical) from some or all of the recordscreated in step S115. In some embodiments, the structure of thedictionary compiled in step S120 may include a pair of items for eachdictionary entry. In an embodiment, a dictionary may be organizedsimilar to that illustrated in FIG. 3.

In FIG. 3, dictionary 300 may include multiple entries that each includea data element 310 (a word, phrase, sentence, quantitative data or otherclass of data from the database records) and a code 315. Code 315 mayalso be referred to as an index. In some embodiments, dictionary 300 mayinclude categorized groups. For example, dictionary 300 may include, butare not limited to, entries for location 320, tags 325,company/organization 330, salary 335, and title 340. In an embodiment,dictionary 300 may contain D entries, where D is the total number ofentries in dictionary 300.

Returning again to FIG. 1, in step S125, process 100 may create a set oftraining input/output pairs, stored in a memory or other storage device(computer readable storage medium), from the records compiled in stepS115. The training input/output pairs may comprise some or allpermutations of all of the code entries within each record. In anembodiment, a set of training input/output pairs may be organizedsimilar to that illustrated in FIG. 4.

In FIG. 4, set of training input/output pairs 400 may include multipleentries that each include an input code 410 and an output code 415. Inan embodiment, as illustrated in FIG. 4, a first group 420 may includean input code shown as “204” with output codes drawn from each of theother codes in the same record. A second group 425 may include an inputcode shown as “2050” with output codes drawn from each of the othercodes in the record. Subsequent entries (not shown) may include groupsrepresenting additional codes from the specific record as an input codewith some or all permutations of other codes in the same record as wellas similar groups drawn from additional records in a database. In anembodiment, a set of training input/output pairs 400 may include allpermutations of the codes for each record of a database. For example, ifa database includes 100 records, and all of the records in the databasehave nine fields (similar to that shown in FIG. 2, for example), thenthe total number of entries N in the set of training input/output pairs400 would be given by equation (1).

N=100*P(9,2)=100*72=7200  (1)

Where P(9,2) represents the number of permutations of nine items taken 2at a time. P(n,k) may be computed using the well-known equation given byequation (2).

$\begin{matrix}{{P\left( {n,k} \right)} = \frac{n!}{\left( {n - k} \right)!}} & (2)\end{matrix}$

Of course records may include any number of fields and a database maycontain an arbitrary number of records.

Returning again to FIG. 1, in step S130, process 100 may compute dataelement embeddings using the set of training input/output pairs createdin step S125. In an embodiment, data element embeddings may be computedusing an artificial neural network. In some embodiments, the traininginput/output pairs may be randomized (reordered into a random order) toimprove the training efficiency of an artificial neural network. In anembodiment, an artificial neural network like that illustrated in FIG. 5may be used to compute data element embeddings.

In FIG. 5, artificial neural network 500 includes an input layer 505, ahidden layer 510, and an output layer 515. For simplicity, input layer505 as illustrated in FIG. 5 shows only six input nodes 508A-508F withan ellipsis. The actual number of nodes in input layer 505 may beselected, for example, to match the number of entries in a dictionary orvocabulary. In an embodiment, the number of actual input nodes in inputlayer 505 may equal the number of entries D in dictionary 300 (shown inFIG. 3). Hidden layer 510 is also shown in simplified form, with fiveneurons 521A-512E and an ellipsis. The actual number of neurons inhidden layer 510 may be selected based on the number of desireddimensions of the embedding, which, in turn, may be determinedempirically based on complexity and size of the dictionary. In someembodiments, the number of dimensions may be in the range of 50 to 1000.

In some embodiments, each neuron in hidden layer 510 receives an inputfrom all of the input nodes in input layer 505. Neuron in hidden layer510 may each sum the inputs received from input layer 505 and, in someembodiments, process the sums through a so-called “nonlinear activationfunction.” The activation function, in an embodiment, may be thewell-known “Softmax” function, or a similar sigmoid or logisticfunction. The outputs from hidden layer 510 may, in some embodiments, beweighted by weights ω₁-ω_(M) (525A-525E) before being summed by outputnode 517 in output layer 515.

In an embodiment, artificial neural network 500 may operate in atraining mode in the following manner: first, an input code 410 for eachof the training pairs 400 (FIG. 4) may be presented at input layer 505.In an embodiment, an input code is presented to input layer 505 byconstructing a so-called “one-hot” vector, where all of the inputs toinput layer 505 are “0” except for the input node corresponding to theinput code 410, which may be set to “1.” For example, if the input code410 is “3,” then input node 508C may receive a “1” and all other inputnodes receive a “0.” For each input code presented to input layer 505,the set of weights 525 may be adjusted to minimize an error between theoutput 520 and the corresponding output code from the training pairs400. In some embodiments, each weight 525 may be a real number.

After all of the entries in the training pairs 400 are presented toartificial neural network 500, and the weights have been adjusted foreach presented training pair, a weight matrix may be formed thatcontains a row of real numbers for each entry in dictionary 300. Each ofthese rows may be extracted as a distributed representation vector(sometimes referred to as a “word vector”) for the associated dictionaryentry. Each distributed representation vector has dimension M, which isthe number of neurons 512 in the hidden layer 510 of artificial neuralnetwork 500.

FIG. 6 illustrates an example weight matrix 600. Weight matrix 600includes D rows, each row representing the weights (ω) computed for eachdata element in dictionary 300 (FIG. 3). For example, row 610 representsthe M weights computed for the second data element in dictionary 300.

FIG. 7 illustrates a simplified vector space view of several distributedrepresentation vectors, as may be computed during training of artificialneural network 500. Vector space 700 (shown as three dimensions—i.e.,M=3—for ease of understanding) includes four distributed representationvectors 710A-710D. In some embodiments, as a result of the trainingdescribed above using training pairs 400, distributed representationvectors 710A and 710B may be found to be in relative proximity to oneanother. And distributed representation vectors 710C and 710C may alsobe found to be in relative proximity to one another.

The present disclosure may be embodied as a system, a method, and/or acomputer program product. The computer program product may include acomputer readable storage medium on which computer readable programinstructions are recorded that may cause one or more processors to carryout aspects of the embodiment.

The computer readable storage medium may be a tangible device that canstore instructions for use by an instruction execution device(processor). The computer readable storage medium may be, for example,but is not limited to, an electronic storage device, a magnetic storagedevice, an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any appropriate combination of thesedevices. A non-exhaustive list of more specific examples of the computerreadable storage medium includes each of the following (and appropriatecombinations): flexible disk, hard disk, solid-state drive (SSD), randomaccess memory (RAM), read-only memory (ROM), erasable programmableread-only memory (EPROM or Flash), static random access memory (SRAM),compact disc (CD or CD-ROM), digital versatile disk (DVD) and memorycard or stick. A computer readable storage medium, as used in thisdisclosure, is not to be construed as being transitory signals per se,such as radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described in this disclosure canbe downloaded to an appropriate computing or processing device from acomputer readable storage medium or to an external computer or externalstorage device via a global network (i.e., the Internet), a local areanetwork, a wide area network and/or a wireless network. The network mayinclude copper transmission wires, optical communication fibers,wireless transmission, routers, firewalls, switches, gateway computersand/or edge servers. A network adapter card or network interface in eachcomputing or processing device may receive computer readable programinstructions from the network and forward the computer readable programinstructions for storage in a computer readable storage medium withinthe computing or processing device.

Computer readable program instructions for carrying out operations ofthe present disclosure may include machine language instructions and/ormicrocode, which may be compiled or interpreted from source code writtenin any combination of one or more programming languages, includingassembly language, Basic, Fortran, Java, Python, R, C, C++, C#, SQL,MySQL, or similar programming languages. The computer readable programinstructions may execute entirely on a user's personal computer,notebook computer, tablet, or smartphone, entirely on a remote computeror compute server, or any combination of these computing devices. Theremote computer or compute server may be connected to the user's deviceor devices through a computer network, including a local area network ora wide area network, or a global network (i.e., the Internet). In someembodiments, electronic circuitry including, for example, programmablelogic circuitry, field-programmable gate arrays (FPGA), or programmablelogic arrays (PLA) may execute the computer readable programinstructions by using information from the computer readable programinstructions to configure or customize the electronic circuitry, inorder to perform aspects of the present disclosure.

Aspects of the present disclosure are described herein with reference toflow diagrams and block diagrams of methods, apparatus (systems), andcomputer program products according to embodiments of the disclosure. Itwill be understood by those skilled in the art that each block of theflow diagrams and block diagrams, and combinations of blocks in the flowdiagrams and block diagrams, can be implemented by computer readableprogram instructions.

The computer readable program instructions that may implement thesystems and methods described in this disclosure may be provided to oneor more processors (and/or one or more cores within a processor) of ageneral purpose computer, special purpose computer, or otherprogrammable apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmableapparatus, create a system for implementing the functions specified inthe flow diagrams and block diagrams in the present disclosure. Thesecomputer readable program instructions may also be stored in a computerreadable storage medium that can direct a computer, a programmableapparatus, and/or other devices to function in a particular manner, suchthat the computer readable storage medium having stored instructions isan article of manufacture including instructions which implement aspectsof the functions specified in the flow diagrams and block diagrams inthe present disclosure.

The computer readable program instructions may also be loaded onto acomputer, other programmable apparatus, or other device to cause aseries of operational steps to be performed on the computer, otherprogrammable apparatus or other device to produce a computer implementedprocess, such that the instructions which execute on the computer, otherprogrammable apparatus, or other device implement the functionsspecified in the flow diagrams and block diagrams in the presentdisclosure.

FIG. 8 is a functional block diagram illustrating a networked system 800of one or more networked computers and servers. In an embodiment, thehardware and software environment illustrated in FIG. 8 may provide anexemplary platform for implementation of the software and/or methodsaccording to the present disclosure.

Referring to FIG. 8, a networked system 800 may include, but is notlimited to, computer 805, network 810, remote computer 815, web server820, cloud storage server 825 and compute server 830. In someembodiments, multiple instances of one or more of the functional blocksillustrated in FIG. 8 may be employed.

Additional detail of computer 805 is shown in FIG. 8. The functionalblocks illustrated within computer 805 are provided only to establishexemplary functionality and are not intend to be exhaustive. And whiledetails are not provided for remote computer 815, web server 820, cloudstorage server 825 and compute server 830, these other computers anddevices may include similar functionality to that shown for computer805.

Computer 805 may be a personal computer (PC), a desktop computer, laptopcomputer, tablet computer, netbook computer, a personal digitalassistant (PDA), a smart phone, or any other programmable electronicdevice capable of communicating with other devices on network 810.

Computer 805 may include processor 835, bus 837, memory 840,non-volatile storage 845, network interface 850, peripheral interface855 and display interface 865. Each of these functions may beimplemented, in some embodiments, as individual electronic subsystems(integrated circuit chip or combination of chips and associateddevices), or, in other embodiments, some combination of functions may beimplemented on a single chip (sometimes called a system on chip or SoC).

Processor 835 may be one or more single or multi-chip microprocessors,such as those designed and/or manufactured by Intel Corporation,Advanced Micro Devices, Inc. (AMD), Arm Holdings (Arm), Apple Computer,etc. Examples of microprocessors include Celeron, Pentium, Core i3, Corei5 and Core i7 from Intel Corporation; Opteron, Phenom, Athlon, Turionand Ryzen from AMD; and Cortex-A, Cortex-R and Cortex-M from Arm.

Bus 837 may be a proprietary or industry standard high-speed parallel orserial peripheral interconnect bus, such as ISA, PCI, PCI Express(PCI-e), AGP, and the like.

Memory 840 and non-volatile storage 845 may be computer-readable storagemedia. Memory 840 may include any suitable volatile storage devices suchas Dynamic Random Access Memory (DRAM) and Static Random Access Memory(SRAM). Non-volatile storage 845 may include one or more of thefollowing: flexible disk, hard disk, solid-state drive (SSD), read-onlymemory (ROM), erasable programmable read-only memory (EPROM or Flash),compact disc (CD or CD-ROM), digital versatile disk (DVD) and memorycard or stick.

Program 848 may be a collection of machine readable instructions and/ordata that is stored in non-volatile storage 845 and is used to create,manage and control certain software functions that are discussed indetail elsewhere in the present disclosure and illustrated in thedrawings. In some embodiments, memory 840 may be considerably fasterthan non-volatile storage 845. In such embodiments, program 848 may betransferred from non-volatile storage 845 to memory 840 prior toexecution by processor 835.

Computer 805 ay be capable of communicating and interacting with othercomputers via network 810 through network interface 850. Network 810 maybe, for example, a local area network (LAN), a wide area network (WAN)such as the Internet, or a combination of the two, and may includewired, wireless, or fiber optic connections. In general, network 810 canbe any combination of connections and protocols that supportcommunications between two or more computers and related devices.

Peripheral interface 855 may allow for input and output of data withother devices that may be connected locally with computer 805. Forexample, peripheral interface 855 may provide a connection to externaldevices 860. External devices 860 may include devices such as akeyboard, a mouse, a keypad, a touch screen, and/or other suitable inputdevices. External devices 860 may also include portablecomputer-readable storage media such as, for example, thumb drives,portable optical or magnetic disks, and memory cards. Software and dataused to practice embodiments of the present disclosure, for example,program 848, may be stored on such portable computer-readable storagemedia. In such embodiments, software may be loaded onto non-volatilestorage 845 or, alternatively, directly into memory 840 via peripheralinterface 855. Peripheral interface 855 may use an industry standardconnection, such as RS-232 or Universal Serial Bus (USB), to connectwith external devices 860.

Display interface 865 may connect computer 805 to display 870. Display870 may be used, in some embodiments, to present a command line orgraphical user interface to a user of computer 805. Display interface865 may connect to display 870 using one or more proprietary or industrystandard connections, such as VGA, DVI, DisplayPort and HDMI.

As described above, network interface 850, provides for communicationswith other computing and storage systems or devices external to computer805. Software programs and data discussed herein may be downloaded from,for example, remote computer 815, web server 820, cloud storage server825 and compute server 830 to non-volatile storage 845 through networkinterface 850 and network 810. Furthermore, the systems and methodsdescribed in this disclosure may be executed by one or more computersconnected to computer 805 through network interface 850 and network 810.For example, in some embodiments the systems and methods described inthis disclosure may be executed by remote computer 815, computer server830, or a combination of the interconnected computers on network 810.

Data, datasets and/or databases employed in embodiments of the systemsand methods described in this disclosure may be stored and or downloadedfrom remote computer 815, web server 820, loud storage server 825 andcompute server 830.

FIGS. 9-14 illustrate aspects of a simplified example of an embodimentof the present disclosure. In this example, a single item of naturallanguage text and data (a job posting) is used as the basis for thegeneration of a set of distributed representation vectors (data elementembedding). In the example, the item of natural language text and dataproduces a record with 16 fields. A simplified dictionary is thengenerated based on these 16 fields. The dictionary is used to producetraining pairs that may then, after randomization, be used to train asimplified artificial neural network to generate the set of distributedrepresentation vectors, each having a dimensionality of four. Asdiscussed above, an actual implementation may generate distributedrepresentation vectors having dimensionality, for example, in the rangeof 50 to 1000.

FIG. 9 illustrates an example item of natural language text,quantitative data, and other data classes (a job posting) that may beone of many such items of a source dataset in an exemplary embodiment ofthe present disclosure. From the job posting 900, various data fieldsmay be extracted (as described above with reference to step S110 in FIG.1). For example, job title 905, company/organization name 910, salaryrange 915, job location 920, and additional fields, such as contactemail 925, contact Fax number 930, job description 933A-933E, requiredregistration 935A, required education 935, and required experience935C-935E.

FIG. 10 illustrates an example record 1000 that may have been populatedfrom data extracted (data elements) from the example item of naturallanguage text, quantitative data, and other data classes (job posting)illustrated in FIG. 9. Record 1000 may include fields 1005-1020 (eachfield represented by a row of the array shown in FIG. 10). In anembodiment, field 1005 may relate to the location of the posted job,field 1006 may relate to the company/organization name associated withthe posted job, field 1007 may relate to the salary associated with theposted job, field 1008 may relate to the job title associated with theposted job, and fields 1009-1020 may relate to additional data (such ascontact information, job descriptions and job requirements) associatedwith the posted job. Each of the fields 1005-1020 within record 1000 mayalso include subfields 1025-1028 (each subfield represented by a columnof the array shown in FIG. 10). In an embodiment, fields 1005-1020 mayinclude subfields that identify a job_id 1030, a code 1035, an element1040, and a category 1045.

The data in example record 1000 in FIG. 10 may be used to compile adictionary like that illustrated in FIG. 11.

In FIG. 11, dictionary 1100 includes 16 entries that each include a dataelement 1110 (a word, phrase, sentence, quantitative data, or otherclasses of data from example record 1000 of FIG. 10) and a code 1115. Inthe simplified example embodiment illustrated in FIG. 11, dictionary1100 contains 16 entries, and the codes are assigned sequentially from 1to 16.

FIG. 12 illustrates a set of training input/output pairs 1200 based onthe dictionary 1000 shown in FIG. 11. As described above with referenceto FIG. 4, training input/output pairs 1200 may include multiple entriesthat each include an input code 1210 and an output code 1215. In anembodiment, as illustrated in FIG. 12, the set of training input/outputpairs 1200 may include all permutations of the codes for each record(for this example, there is only a single record employed). In theexample, with one records having 16 fields (shown in FIG. 10), then thetotal number of entries N in the set of training input/output pairs 1200would be 240 based on the formula given by equation (1). Also shown inFIG. 12 is a set of randomized training input/output pairs 1220,generated by randomly reordering the entries in the set of traininginput/output pairs 1200. The purpose of this randomization, as discussedabove with reference to step S130 in FIG. 1, is to improve the trainingefficiency of an artificial neural network.

FIG. 13 illustrates an example of converting randomized traininginput/output pairs 1300 into “one-hot” vectors used for training anartificial neural network. In FIG. 13, entry 1305 of randomized traininginput/output pairs 1300 may be converted to one-hot input/output pair1310. As may be observed, the input code of entry 1305 (“2”) isconverted to one-hot input vector 1311, with a “1” in the secondposition and a “0” in every other position. Similarly, the output codeof entry 1305 (“9”) is converted to one-hot output vector 1312, with a“1” in the ninth position and a “0” in every other position. In asimilar fashion, entries 1315-1315 are converted to one-hot input/outputpairs 1320-1360. The remaining entries (not shown) in randomizedtraining input/output pairs 1300 are also converted to one-hotinput/output pairs.

The one-hot input/output pairs may then be used to train an artificialneural network like that shown in FIG. 5. For this example, theartificial neural network would have 16 input nodes and 4 hiddenneurons. Each of the one-hot input vectors would be presented to theartificial neural network, and the network would adjust the appropriateweights (525A, etc.), using for example, back propagation and gradientdescent optimization, to produce the corresponding one-hot outputvectors.

After all of the 240 training pairs in this example embodiment, havebeen used to train the artificial neural network, a weight matrix 1400,illustrated in FIG. 14, may be produced. Weight matrix 1400 includes 16rows, each row representing the distributed representation vector (groupof weights) computed for each data element in dictionary 1100 (FIG. 11).For example, row 1410 represents the distributed representation vectorcomputed for the fourth dictionary entry (“Patent Attorney orAgent—Biotechnology”).

The use of the embodiments disclosed herein allow for improvements andenhancements to the traditional use of computers in the field of naturallanguage processing of textual, quantitative, and other classes of. Thedistributed representation vectors generated by the disclosedembodiments allow for direct manipulation, comparison and association ofcomplex terms (words, phrases, sentences, quantitative data, and otherclasses of data) by using vector arithmetic (addition, subtraction,etc.) and vector distance measures (dot product, cosine similarity,Euclidean distance, etc.). For example, these techniques may be used inthe area of human resource management to produce better understanding ofhuman capital trends and draw better insights about job skills,occupational trends, company value, and career paths. Additionally,these techniques may provide insights to companies, organizations,governments and individuals to better understand labor markets.Furthermore, the ability of the disclosed embodiments to representquantitative data facilitates the derivation of quantitative formulae,distributions, functions, and the like, that have not previously beenable to be modeled due to a heavy dependency on context. With theembodiments disclosed herein, it is possible, for example, to define afunction for a job's salary where the independent variables are factorslike company name, job location, job title and tags. Finally, by firstparsing source data into component classes (e.g., company name, joblocation, job title, salary, tags), the disclosed embodiments allowrelational representations to be discovered within each class (e.g.,‘multi-task’ is a synonym of ‘multitask’) and in combination (e.g.,‘Blockchain Developer’ at Company A equals ‘SoftwareEngineer’+‘Blockchain’ at Company B).

Those with skill in the art will also recognize that the embodimentsdisclosed herein allow for extremely efficient processing of scenariosthat are necessarily defined across multiple classes (e.g. each scenariois defined having a company name, job title, job location, salary, andtags). For example, in many real-world applications, documents are oftenstructured with multiple mandatory and optional classes of textual,quantitative, and other non-text data types (e.g., a job posting musthave an identifying code, company name, job title, description,experience requirements, location, and may include salary, benefits,education requirements, certification requirements, contact information,marketing content, code snippets, other meta-data, etc.), so analyzingrelationships between and among scenarios (e.g., jobs or any combinationof component classes) must address these classes of information. Theembodiments disclosed herein allow for processing of substantially morecomplex problems than traditional Natural Language Processing systemswith no increase in computation or memory requirements. Theseembodiments may also be viewed as reducing the complexity for multipleclass scenarios to be equal to the complexity of single class scenarios,or substantially reducing complexity of 1:1 mapping approaches to closeto zero. Mapping complexity is therefore, using the disclosedembodiments, independent of the number of classes being represented.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

Obviously, numerous modifications and variations of the presentinvention are possible in light of the above teachings. It is thereforeto be understood that within the scope of the appended claims, theinvention may be practiced otherwise than as specifically describedherein.

1. A method for generation, by a processor, of a set of distributedrepresentation vectors, the method comprising: receiving, by theprocessor, a dataset, wherein the dataset comprises a set of relateditems of at least one of natural language text and non-text data;parsing, by the processor, the dataset to identify known classes ofdata; extracting data elements from the dataset based on the knownclasses of data; organizing the extracted data elements into one or morerecords; compiling, by the processor, a dictionary of unique dataelements and associated codes from the one or more records; creating aset of training data using permutations of the codes that correspond toelements within each record; and computing, by the processor, adistributed representation vector for each of the data elements in thedictionary using the set of training data, wherein: each of the relateditems is for a job position, and the known classes of data include anorganization name and a job title.
 2. The method of claim 1, wherein theknown classes of data further include a job location, a salary, and ajob description.
 3. The method of claim 1, further comprising: cleaningthe dataset prior to parsing, wherein cleaning the dataset additionallycomprises one or more of: removing extraneous data, removing irrelevantdata, removing inaccurate data, correcting corrupt data and correctinginaccurate data in the dataset.
 4. The method of claim 1, whereincreating the set of training data uses all possible permutations ofcodes that correspond to data elements within each record.
 5. The methodof claim 1, wherein computing the distributed representation vectorsuses an artificial neural network with one hidden layer, wherein a setof weights computed in the hidden layer form the distributedrepresentation vectors.
 6. A computer program product for generating aset of distributed representation vectors of a dataset of at least oneof natural language text and non-text data, the computer program productcomprising a computer readable storage medium having stored thereonprogram instructions which, when executed by one or more processors,cause the one or more processors to: receive a dataset, wherein thedataset comprises a set of related items of at least one of naturallanguage text and non-text data; parse the dataset to identify knownclasses of data; extract data elements from the dataset based on theknown classes of data; organize the extracted data elements intorecords; compile a dictionary of unique data elements and associatedcodes from one or more of the records; create a set of training datausing permutations of the codes that correspond to data elements withineach record; and compute a distributed representation vector for each ofthe data elements in the dictionary using the set of training data,wherein: each of the related items is for a job position, and the knownclasses of data include an organization name and a job title.
 7. Thecomputer program product of claim 6, wherein the known classes of datafurther include a job location, a salary, and a job description.
 8. Thecomputer program product of claim 6, wherein the program instructionsfurther cause the one or more processors to clean the dataset, prior toparsing, and wherein the program instructions that cause the one or moreprocessors to clean the dataset, additionally comprise programinstructions that cause the one or more processors to remove one or moreof: extraneous data, irrelevant data, inaccurate data, corrupt data andinaccurate data from the dataset.
 9. The computer program product ofclaim 6, wherein the program instructions that cause the one or moreprocessors to create a set of training data use all possiblepermutations of codes that correspond to data elements within eachrecord.
 10. The computer program product of claim 6, wherein the programinstructions that cause the one or more processors to compute thedistributed representation vectors additionally comprise programinstructions that cause the one or more processors to use an artificialneural network with one hidden layer, wherein a set of weights computedin the hidden layer form the distributed representation vectors.
 11. Acomputer system for generating a set of distributed representationvectors of a dataset of at least one of natural language text andnon-text data, the computer system comprising: one or more processors;and a computer readable storage medium, wherein the one or moreprocessors is configured to execute program instructions stored on thecomputer readable storage medium and the program instructions include:program instructions to receive a dataset, wherein the dataset comprisesa set of related items of at least one of natural language text andnon-text data; program instructions to parse the dataset to identifyknown classes of data; program instructions to extract data elementsfrom the dataset based on the known classes of data; programinstructions to organize the extracted data elements into records;program instructions to compile a dictionary of unique data elements andassociated codes from one or more of the records; program instructionsto create a set of training data using permutations of the codes thatcorrespond to data elements within each record; and program instructionsto compute a distributed representation vector for each of the dataelements in the dictionary using the set of training data, wherein: eachof the related items is for a job position, and the known classes ofdata include an organization name and a job title.
 12. The computersystem of claim 11, wherein the known classes of data further include ajob location, a salary, and a job description.
 13. The computer systemof claim 11, wherein the program instructions further cause the one ormore processors to clean the dataset, prior to parsing, and the programinstructions to clean the received dataset, additionally compriseprogram instructions to remove one or more of: extraneous data,irrelevant data, inaccurate data, non-data data, corrupt data andinaccurate data from the dataset.
 14. The computer system of claim 11,wherein the program instructions to create a set of training data useall possible permutations of codes that correspond to data elementswithin each record.
 15. The computer system of claim 11, wherein theprogram instructions to compute the distributed representation vectorsadditionally comprise program instructions to use an artificial neuralnetwork with one hidden layer, wherein a set of weights computed in thehidden layer form the distributed representation vectors.